Previous studies have shown that the CareStart™ G6PD Deficiency rapid diagnostic test has high diagnostic accuracy on G6PD deficiency in Africa and Thailand, but not in China. As there are regional differences of G6PD genotype distribution, we are attending to verify the effectiveness of the kit in Chinese population. The study cohort included 247 newborns admitted to our hospital for jaundice. The quantitative detection of G6PD enzyme activity and G6PD gene mutations analysis was used to classify the status of G6PD deficiency. The performance of CareStart™ assays was verified by calculating the sensitivity, specificity and area under the receiver operating characteristic curve (AUC) based on the corrected G6PD deficiency status. In male newborns, the sensitivity of the CareStart™ assay was 98.9%, the specificity was 94.2% and the AUC was 0.97. In female newborns, the sensitivity was 58.5% when the cutoff value of residual enzyme activity was 100%; however, the sensitivity was 100% when the cutoff value was 60%. Therefore, the CareStart™ test can effectively screen G6PD deficiency in male newborns and female infants with less than 60% residual enzyme activity, female infants with residual enzyme activities of 60–100% are more likely to be missed diagnosed among Chinese newborns.

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Background. Diabetic nephropathy (DN), a significant cause of chronic kidney disease (CKD), is a devastating disease worldwide. Objective. The aim of this study was to reveal crucial genes closely linked to the molecular mechanism of tubulointerstitial injury in DN. Methods. The Gene Expression Omnibus (GEO) database was used to download the datasets. Based on this, a weighted gene coexpression network analysis (WGCNA) network was constructed to detect DN-related modules and hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichments were performed on the selected hub genes and modules. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed on the obtained gene signature. Results. The WGCNA network was constructed based on 3019 genes, and nine gene coexpression modules were generated. A total of 57 genes, including 34 genes in the magenta module and 23 genes in the purple module, were adapted as hub genes. 61 significantly downregulated and 119 upregulated genes were screened as differentially expressed genes (DEGs). 25 overlapping genes between hub genes chosen from WGCNA and DEG were identified. Through LASSO analysis, a 9-gene signature may be a potential prognostic biomarker for DN. To further explore the potential mechanism of DN, the different immune cell infiltrations between tubulointerstitial samples of DN and healthy samples were estimated. Conclusions. This bioinformatics study identified CX3CR1, HRG, LTF, TUBA1A, GADD45B, PDK4, CLIC5, NDNF, and SOCS2 as candidate biomarkers for the diagnosis of DN. Moreover, DN tends to own a higher proportion of memory B cell.

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